EP3513211A1 - Procédé de post-traitement d'images irm en vue d'obtenir des paramètres de perfusion et de transport hépatiques - Google Patents

Procédé de post-traitement d'images irm en vue d'obtenir des paramètres de perfusion et de transport hépatiques

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Publication number
EP3513211A1
EP3513211A1 EP17764415.0A EP17764415A EP3513211A1 EP 3513211 A1 EP3513211 A1 EP 3513211A1 EP 17764415 A EP17764415 A EP 17764415A EP 3513211 A1 EP3513211 A1 EP 3513211A1
Authority
EP
European Patent Office
Prior art keywords
post
subject
processing images
liver
parameters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP17764415.0A
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German (de)
English (en)
Inventor
Benjamin LEPORQ
Jean-Luc DAIRE
Bernard VAN BEERS
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Centre National de la Recherche Scientifique CNRS
Assistance Publique Hopitaux de Paris APHP
Institut National de la Sante et de la Recherche Medicale INSERM
Universite Paris Diderot Paris 7
Original Assignee
Centre National de la Recherche Scientifique CNRS
Assistance Publique Hopitaux de Paris APHP
Institut National de la Sante et de la Recherche Medicale INSERM
Universite Paris Diderot Paris 7
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Publication of EP3513211A1 publication Critical patent/EP3513211A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/56366—Perfusion imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A61B5/0263—Measuring blood flow using NMR
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
    • A61B5/0275—Measuring blood flow using tracers, e.g. dye dilution
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4222—Evaluating particular parts, e.g. particular organs
    • A61B5/4244—Evaluating particular parts, e.g. particular organs liver
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/5601—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/5635—Angiography, e.g. contrast-enhanced angiography [CE-MRA] or time-of-flight angiography [TOF-MRA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2576/00—Medical imaging apparatus involving image processing or analysis
    • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
    • A61B5/7289—Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30056—Liver; Hepatic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Definitions

  • the present invention concerns a method for post-processing images of a region of interest of a subject.
  • the invention concerns a method for predicting that a subject is at risk of suffering from a liver disease.
  • the invention also relates to a method for diagnosing a liver disease.
  • the invention also concerns a method for identifying a therapeutic target for preventing and/or treating a liver disease.
  • the invention also relates to a method for identifying a biomarker, the biomarker being a diagnostic biomarker of a liver disease, a susceptibility biomarker of a liver disease, a prognostic biomarker of a liver disease or a predictive biomarker in response to the treatment of a liver disease.
  • the invention also concerns a method for screening a compound useful as a medicine, the compound having an effect on a known therapeutical target, for preventing and/or treating a liver disease.
  • the invention also relates to the associated computer program products and a computer readable medium.
  • Liver diseases concern a high number of people in the world.
  • Liver diseases notably encompass chronic liver disease and liver cancer (a liver primitive cancer or metastasis)
  • the main issue comes from a double choice: the choice of the properties to be measured and the choice of the techniques to be used for measuring the properties.
  • liver disease it is desirable to obtain information on the tissue cellularity, their perfusion, the degree of molecular transport in hepatocytes and their visco-elasticity.
  • the tissue perfusion may be investigated by using dynamic contrast-enhanced MRI, or perfusion computed tomography and the transport function of the hepatocytes may be measured by using gadoxetate MRI enhancement analyses or blood tests such as aminotransferase, bilirubin or gamma glutamyl transferase levels.
  • the invention aims at providing a method which can be achieved in only one experiment and provides the best accuracy in the prediction of the risk for a subject to suffer from a liver disease.
  • liver disease it is meant a disease that affects the liver.
  • the liver disease is one of a chronic liver disease, a liver cancer.
  • Chronic liver disease in the clinical context is a disease process of the liver that involves a process of progressive destruction and regeneration of the liver parenchyma leading to fibrosis and cirrhosis.
  • Chronic liver disease refers to disease of the liver which lasts over a period of six months. It consists of a wide range of liver pathologies which include inflammation (chronic hepatitis), liver cirrhosis, and hepatocellular carcinoma.
  • Liver cancer also known as hepatic cancer, is a cancer that originates in the liver. Liver tumors are discovered on medical imaging equipment or present themselves symptomatically as an abdominal mass, abdominal pain, yellow skin, nausea or liver dysfunction. The leading cause of liver cancer is cirrhosis due to either hepatitis B, hepatitis C, or alcohol.
  • the liver cancer is a liver primitive cancer or a liver metastasis.
  • the specification describes a method for post-processing images of a region of interest in a subject to obtain determined parameters, the determined parameters comprising at least one perfusion parameter and at least one transport parameter, the perfusion parameters being relative to the hepatic perfusion and the transport parameter being relative to the hepatic function transport, the images being acquired with a magnetic resonance imaging technique, the magnetic resonance imaging technique being enhanced by a contrast agent, the magnetic resonance imaging technique involving successive echoes of a multiple-gradient echo sequence, each image associating to each pixel of the image the amplitude of the measured signal in the magnetic resonance imaging technique and the phase of the measured signal in the magnetic resonance imaging technique.
  • the method for post-processing comprising at least the phase of extracting a time intensity-curve or signal intensity according to the time for at least one pixel of the images, to obtain at least one time-intensity curve, the phase of converting the time-intensity curves in a concentration signal, a concentration signal being a signal representative of the evolution of the contrast agent concentration with time, the phase of calculating the at least one perfusion parameter and the at least one transport parameter by using a fitting procedure applied on a model, the model being a function which associates to a plurality of parameters each concentration signal, the plurality of parameters being parameters which characterizes the kinetics of the elimination of the contrast agent by the liver, the liver being represented as a three- compartment organ with an extracellular compartment, a hepatocyte compartment and the intra-hepatic bile ducts, the plurality of parameters comprising at least one perfusion parameter and at least one transport parameter.
  • the fitting procedure is applied in two steps: a first step during which several parameters of the model are set to zero, the model becoming a simplified model corresponding to the liver being represented as a two- compartment organ with an extracellular compartment and a hepatocyte compartment only, to obtain determined parameters with a determined value and a second step during which several parameters are set the determined value, to obtain the at least one perfusion parameter and the at least one transport parameter.
  • Each step is achieved with a fitting technique being a non-linear least-square fitting technique using pseudo-random initial conditions.
  • This technique includes two inputs in the extracellular compartment of the quantification model rather only one in the article from C. Giraudeau et al. Therefore, the technique presented in this application is more "physiologic" since the liver has two vascular supplies (arterial and portal).
  • the described technique rather than deconvolution-based approaches such as used in the Giraudeau et al method, the described technique used a dedicated data fitting procedure and allow to measure perfusion parameters. These latter are also known to be clinically relevant to assess liver fibrosis severity.
  • the method for post-processing images might incorporate one or several of the following features, taken in any technically admissible combination: - the first step is applied on images during a first interval of time and the second step is applied on images during a second interval of time, the second interval of time including the first interval of time.
  • the ratio of the first interval of time to the second interval of time is inferior to 25%.
  • the first interval of time is comprises between 5 minutes to 10 minutes.
  • the phase of converting comprises a step of converting the concentration of contrast agent concentration in blood into the concentration of contrast agent concentration in plasma.
  • the phase of converting comprises a step of interpolating the concentration signal.
  • the plurality of parameters comprises the rate of exchanges between each of the three compartments.
  • the calculating phase comprises a step of calculating at least one of the hepatocyte uptake fraction, the extracellular mean transit time and the extracellular volume based on the determined at least one perfusion parameter and the at least one transport parameter, the calculated parameter being one of determined parameter.
  • the region of interest includes a part of the liver.
  • the specification describes a method for predicting that a subject is at risk of suffering from a liver disease, the method for predicting at least comprising the step of carrying out the steps of a method for post-processing images of the subject, to obtain determined parameters and the step of predicting that the subject is at risk of suffering from the liver disease based on the determined parameters.
  • Another application of such method for predicting is notably to evaluate the risk of postoperative liver failure after major liver resection.
  • the postoperative liver failure after major liver resection is considered to be a liver disease in this context.
  • the specification also relates to a method for diagnosing a liver disease, the method for diagnosing at least comprising the step of carrying out the steps of a method for post-processing images of the subject, to obtain determined parameters and the step of diagnosing the liver disease based on the determined parameters.
  • the specification also concerns a method for identifying a therapeutic target for preventing and/or treating a liver disease, the method comprising the step of carrying out the steps of a method for post-processing images of a first subject, to obtain first determined parameters corresponding to the determined parameters for the first subject, the first subject being a subject suffering from the liver disease.
  • the method for identifying further comprising the step of carrying out the steps of the method for post-processing images of a second subject, to obtain second determined parameters corresponding to the determined parameters for the second subject, the second subject being a subject not suffering from the liver disease, and the step of selecting a therapeutic target based on the comparison of the first and second determined parameters.
  • the specification also relates to a method for identifying a biomarker, the biomarker being a diagnostic biomarker of a liver disease, a susceptibility biomarker of a liver disease, a prognostic biomarker of a liver disease or a predictive biomarker in response to the treatment of a liver disease, the method comprising the step of carrying out the steps of a method for post-processing images of a first subject, to obtain first determined parameters, the first determined parameters corresponding to the determined parameters for the first subject, the first subject being a subject suffering from the liver disease.
  • the method for identifying further comprising the step of carrying out the steps of the method for post-processing images of a second subject, to obtain second determined parameters corresponding to the determined parameters for the second subject, the second subject being a subject not suffering from the liver disease, and the step of selecting a biomarker based on the comparison of the first and second determined parameters.
  • the specification also concerns a method for screening a compound useful as a medicine, the compound having an effect on a known therapeutical target, for preventing and/or treating a liver disease, the method comprising a step of carrying out the steps of a method for post-processing images of a first subject, to obtain first determined parameters, the first determined parameters corresponding to the determined parameters for the first subject, the first subject being a subject suffering from the liver disease and having received the compound.
  • the method also comprises a step of carrying out the steps of the method for post-processing images of a second subject, to obtain second determined parameters, the second determined parameters corresponding to the determined parameters for the second subject, the second subject being a subject suffering from the liver disease and not having received the compound.
  • the method also comprises a step of selecting a compound based on the comparison of the first and second determined parameters.
  • the specification also describes a computer program product comprising instructions for carrying out the steps of any method chosen among the previously described method for post-processing, method for predicting, method for diagnosing, method for identifying a therapeutic target, method for identifying a biomarker and the method for screening a compound useful as a medicine when said computer program product is executed on a suitable computer device.
  • the specification also describes a computer readable medium having encoded
  • FIG. 1 shows schematically a system and a computer program product whose interaction enables to carry out a method for post-processing images
  • FIG. 2 shows a flowchart of the method for post-processing images
  • FIG. 3 shows a schematic view of a pharmacokinetic model used in the method for post-processing images
  • a system 10 and a computer program product 12 are represented in figure 1 .
  • the interaction between the computer program product 12 and the system 10 enables to carry out a method for post-processing images.
  • System 10 is a computer. In the present case, system 10 is a laptop.
  • system 10 is a computer or computing system, or similar electronic computing device adapted to manipulate and/or transform data represented as physical, such as electronic, quantities within the computing system's registers and/or memories into other data similarly represented as physical quantities within the computing system's memories, registers or other such information storage, transmission or display devices.
  • System 10 comprises a processor 14, a keyboard 22 and a display unit 24.
  • the processor 14 comprises a data-processing unit 16, memories 18 and a reader 20.
  • the reader 20 is adapted to read a computer readable medium.
  • the computer program product 12 comprises a computer readable medium.
  • the computer readable medium is a medium that can be read by the reader of the processor.
  • the computer readable medium is a medium suitable for storing electronic instructions, and capable of being coupled to a computer system bus.
  • Such computer readable storage medium is, for instance, a disk, a floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs) electrically programmable read-only memories (EPROMs), electrically erasable and programmable read only memories (EEPROMs), magnetic or optical cards, or any other type of media suitable for storing electronic instructions, and capable of being coupled to a computer system bus.
  • a computer program is stored in the computer readable storage medium.
  • the computer program comprises one or more stored sequence of program instructions.
  • the computer program is loadable into the data-processing unit and adapted to cause execution of the method for post-processing images when the computer program is run by the data-processing unit.
  • the images post-processed in the method for post-processing images are images of a region of interest in a subject.
  • the region of interest includes a part of the liver.
  • the subject is usually human beings.
  • the subject can also be animals, such as mice, rats, rabbits or primates...
  • the images are acquired with a magnetic resonance imaging technique.
  • the magnetic resonance imaging technique being enhanced by a contrast agent
  • the contrast agent is gadoxetate.
  • all hepatospecific contrast media can be used as a contrast agent.
  • Gd-BOPTA may be used.
  • the magnetic resonance imaging technique involving a dynamic contrast- enhanced acquisition.
  • the dynamic contrast-enhanced acquisition is a gradient echo sequence.
  • the magnetic resonance imaging technique is carried out by a clinical system operating at magnetic field with a magnitude of 3.0 Tesla (T).
  • Each image associates to each pixel of the image the amplitude of the measured signal in the magnetic resonance imaging technique.
  • the method for post-processing images enables to obtain determined parameters which are detailed below.
  • the determined parameters are perfusion parameters and liver transport function parameters.
  • Perfusion parameters include the total perfusion which represents the contribution of arterial and portal perfusion, the hepatic perfusion index which represents the part or arterial perfusion over the total perfusion, the extracellular mean transit time which is the inverse of the venous transfer rate and the extracellular volume.
  • Liver transport function parameters include the hepatocyte uptake fraction, the sinusoidal backflux and the biliary efflux.
  • the method for post-processing images comprises three phases P1 , P2 and P3, which are a phase P1 of extracting, a phase P2 of converting and a phase P3 of calculating.
  • a time-intensity curve for at least one pixel of the images is extracted.
  • a signal intensity over the time S(t) is extracted pixel by pixel.
  • the extracted signal is converted into in a concentration signal.
  • a concentration signal is a signal representative of the evolution of the contrast agent concentration with time.
  • the phase of converting P2 comprises a step of converting the concentration of contrast agent concentration in blood into the concentration of contrast agent concentration in plasma.
  • the input functions may be normalized by one minus hematocrit, the hematocrit being set to 45%. Since gadoxetate does not diffuse into red blood and to take into account only plasmatic exchange between input function and extracellular compartment in the liver, input functions were normalized by one minus the hematocrit.
  • the phase of converting P2 further comprises a step of interpolating the concentration signal.
  • the interpolation is, for instance, achieved by using spline curves.
  • the at least one perfusion parameter and the at least one transport parameter are calculated.
  • the phase of calculating P3 enables to calculate the at least one perfusion parameter and the at least one transport parameter by using a fitting procedure applied on a model.
  • the model is a function which associates to a plurality of parameters each concentration signal.
  • the plurality of parameters are parameters which characterizes the kinetics of the elimination of the contrast agent by the liver, the liver being represented as a three- compartment organ with an extracellular compartment, a hepatocyte compartment and the intra-hepatic bile ducts.
  • the plurality of parameters comprises at least one perfusion parameter and at least one transport parameter.
  • the model is thus a pharmacokinetic model modeling the kinetic of elimination of the tracer and is schematically illustrated by figure 3.
  • the liver is a dual input three compartments.
  • the three compartments are the extracellular compartment, the hepatocyte compartment and the intrahepatic bile ducts.
  • the extracellular compartment comprises the intravascular compartment and the Disse space's.
  • the hepatocyte compartment is also named the cellular compartment.
  • the extracellular compartment is the first compartment
  • the hepatocyte compartment is the second compartment
  • the intrahepatic bile ducts is the third compartment.
  • the gadoxetate In normal operating, the gadoxetate first enters in the extracellular compartment by arterial and portal inputs according arterial and portal perfusion. A fraction of gadoxetate uptake into the hepatocyte where it can be excreted into the intrahepatic bile duct or redistributed into the extracellular compartment by sinusoidal backflux.
  • the non-uptake fraction of tracer directly washes out the liver through the hepatic veins to be next redistributed according to a venous transfer rate.
  • ⁇ C(t) is the concentration of the tracer in the plasma. More precisely, C(t) is the concentration in the liver (more precisely in the region of interest). Concentration in the liver includes concentration in extracellular compartment (plasma + Disse space), in the hepatocyte, and in the intrahepatic bile duct.
  • ⁇ F is the blood flow also named total perfusion
  • HPI is the hepatic perfusion index expressing the part of arterial perfusion over total perfusion F
  • ⁇ ⁇ ⁇ is the arterial delay corresponding to the temporal offset between the true input in the liver and measured input from arterial perfusion
  • ⁇ ⁇ is the portal delay corresponding to the temporal offset between the true input in the liver and measured input from portal perfusion
  • k 32 denotes the portion transferred from the hepatocyte compartment to the intrahepatic bile ducts
  • c is a coefficient representing the difference of volume between the hepatocyte and the intrahepatic ducts
  • F, HPI, k01 are perfusion parameters.
  • K21 , k12 and k3 are hepatic transport function parameters.
  • the model is the following function:
  • C(t) F[C A (t - ⁇ ⁇ ) HPI + C P (t - ⁇ ⁇ )(1 - HPI)] ® e -k ⁇ .01
  • the phase of calculating P3 comprises, according to the example of figure 2, a step of obtaining S50 the arterial and portal delays, a first step S60 of fitting procedure, a second step S70 of fitting procedure and a third step S80 of calculating.
  • the arterial delay is calculated by using the figure 4 which represents the arterial input function (curve C1 ) portal input function (curve C2) and tissue response (other curve) recorded after retrospective respiratory motion correction.
  • curve C1 the arterial input function
  • curve C2 portal input function
  • tissue response other curve
  • the arterial delay is measured as the temporal difference between the beginning of the arterial input and tissue response increases and portal delay is set to zero.
  • Arterial input function is extracted from a region of interest drawn by a user in the abdominal aorta.
  • Portal input function is extracted from a region of interest drawn by a user in the main portal vein.
  • Tissue response is extracted from a region of interest drawn by a user in the liver parenchyma.
  • the simplified model M1 can be written as:
  • Tissue mass volume is accounted by multiplying by the tissue mass volume (equal to 1 ) such as described previously.
  • the simplified model M1 is a dual input bi compartment uptake model.
  • the simplified model corresponds to the liver being represented as a two-compartment organ with an extracellular compartment and a hepatocyte compartment only.
  • the second model M2 is written as follows:
  • C(t) F[C A (t - ⁇ ⁇ ) HPI + C P (t - ⁇ ⁇ )(1 - HPI)] ® e -k ⁇ .01
  • the venous transfer rate k 0 i is a free parameter, which means that the value of the first step is discarded without being used.
  • the fitting technique is again a non-linear least-square fitting technique using pseudo-random initial conditions
  • the first step S60 is applied on images during a first interval of time and the second step S70 is applied on images during a second interval of time, the second interval of time including the first interval of time.
  • the first interval of time comprises times which are each times of the second interval of time.
  • the ratio of the first interval of time to the second interval of time is inferior to 25%.
  • the first interval of time is comprises between 5 minutes to 10 minutes.
  • the first interval of times comprises times which are inferior to 8 minutes while the second interval of times comprises times which are inferior to 38 minutes.
  • the calculating phase further comprises the step of calculating S80 at least one of the hepatocyte uptake fraction, the extracellular mean transit time and the extracellular volume based on the determined at least one perfusion parameter and the at least one transport parameter, the calculated parameter being one of determined parameter.
  • the three parameters that is the hepatocyte uptake fraction, the extracellular mean transit time and the extracellular volume are calculated based on the parameters F, k 0 i and k 2 i .
  • hepatocyte uptake fraction E in % is calculated using the following equation:
  • the hepatocyte uptake fraction, the extracellular mean transit time and the extracellular volume are determined at the calculating phase.
  • the method enables to access simultaneously at least one perfusion parameter and at least one transport parameter based on a single MRI acquisition during which the patient is authorized to breath.
  • the provided method can be achieved in only one experiment and provides the best accuracy in the prediction of the risk for a subject to suffer from a liver disease.
  • Such method may be used to study the treatment response in a liver disease.
  • This method can be used to study the treatment response in liver oncology.
  • This method can be used to study the treatment response in chronic liver diseases.
  • This method can be used to assess non-invasively the liver function before major liver resection to minimize the risk of post-operative liver failure, especially in patients with cirrhosis or even less advanced chronic diseases.
  • this method aims to provide with biomarkers of hepatic function and microperfusion which may be useful in liver oncology, in chronic liver disease or before major liver surgery.
  • the method for post-processing may be used advantageously in other methods, the adaptation to these methods being immediate.
  • liver transport function is important to determine the prognosis of patients with chronic liver diseases, and is particularly required in patients with cirrhosis to determine the optimal timing for transplantation and determine whether or not transjugular intrahepatic portosystemic shunt insertion is contraindicated. Evaluation of total and regional liver function is also needed before major liver resection to minimize the risk of post-operative liver failure, especially in patients with cirrhosis or even less advanced chronic diseases such as steatosis, cholestasis, or chemotherapy toxicity. During the eighties, imaging has been proposed to quantify the hepatic transport function such as scintigraphy following the injection of the hepatobiliary tracer technetium- 99 m iminodiacetic acid.
  • the hepatocyte gadoxetate uptake fraction and the input relative blood flow was estimated with a deconvolution method.
  • a hybrid approach combining a deconvolution for plasma flow map calculation then pharmacokinetic modeling it has been shown that it is possible to simultaneously measure hepatic micro-perfusion and hepatocyte gadoxetate uptake fraction.
  • information about biliary excretion may provide a relevant clinical information
  • any approaches are able to simultaneously quantify micro-perfusion and the whole hepatic transport function (i.e the hepatocyte uptake and the biliary excretion).
  • the aims of this study are to develop a method able to simultaneously quantify the hepatic micro-perfusion and the whole transport function with MR dynamic gadoxetate enhanced imaging.
  • the clinical feasibility will be investigated in vivo, in patients with chronic liver diseases.
  • liver biopsy was performed through a transjugular approach using a catheter needle with a diameter of 1 .1 mm (Cook Bloomington, Indiana, USA).
  • the liver samples were fixed in buffered formalin and embedded in paraffin. Sections, 4 ⁇ thick, were stained with hematoxylin-eosin and Masson trichrome and assessed by a pathologist blinded to the clinical data and to the results of MR imaging.
  • the stage of fibrosis from FO to F4 was evaluated semiquantitatively on Masson trichrome-stained slides according to the METAVIR staging system.
  • MR imaging was performed on a Siemens Skyra 3.0 T system (Siemens Medical Solutions, Er Weg, Germany). Dynamic acquisition was achieved with a three- dimensional time-resolved angiography with interleaved stochastic trajectories (TWIST) sequence employing the generalized autocalibrating partial parallel acquisition (GRAPPA). The signal was collected with the 32-channel phased array body and the spine coils.
  • the acquisition parameters were:
  • the acquisition plane was coronal with the following geometric parameters: field-of-view, 375 ⁇ 300 ⁇ 120 mm 3 ; acquisition / reconstruction matrix, 140 ⁇ 160 ⁇ 24 / 200 ⁇ 160 ⁇ 40 with right / left phase encoding direction.
  • TWIST temporal resolution was 2.1 s.
  • Gadoxetate (Primovist, Bayer, Berlin, Germany) was injected intravenously using an automatic injector (Medrad Spectris Solaris, Warrendale, PA) with an injection rate of 1 mL.s "1 and flushed with physiologic saline (same volume and injection rate).
  • the gadoxetate dose was 0.025 mmol.kg 1 .
  • Dynamic liver imaging was started simultaneously with the intravenous injection of gadoxetate and was performed in free breathing. Since gadoxetate kinetic decreases along the time after bolus injection (that is faster during perfusion phase than during uptake phase than during biliary excretion phases), the temporal resolution was artificially decreased over the time to minimize the total number of images.
  • pauses were added between dynamics as follow: for the 280 first dynamics: no pause, the effective temporal resolution was 2.1 s; for the next 150 dynamics: 5s pause, the effective temporal resolution was 7.1 s, for the 60 last dynamics; 8s pause, the effective temporal resolution was 10.1 s. Dynamic acquisition duration was close to 38 minutes.
  • liver By assuming the liver as a time-invariant, causal, linear and stationary dynamic system:
  • R(t) is the residue function which represents the gadoxetate fraction still present in the liver along the time t.
  • R(t) is equal to 1 - /* ⁇ ( ⁇ ) ⁇ .
  • liver having a double input (arterial and portal)
  • concentration of gadoxetate into the incoming blood can be expressed as:
  • HPI is the hepatic perfusion index expressing the part of arterial perfusion over total perfusion F
  • ⁇ ⁇ is the arterial delay corresponding to the temporal offset between the true input in the liver and measured input from arterial perfusion
  • ⁇ ⁇ is the portal delay corresponding to the temporal offset between the true input in the liver and measured input from portal perfusion.
  • the liver is modeled with a model according to which the liver is a dual input three compartments.
  • the three compartments are the extracellular compartment, the hepatocyte compartment and the intrahepatic bile ducts.
  • the extracellular compartment comprises the intravascular compartment and the Disse space's.
  • the gadoxetate In normal operating, the gadoxetate first enters in the extracellular compartment by arterial and portal inputs according arterial and portal perfusion. A fraction of gadoxetate uptake into the hepatocyte where it can be excreted into the intrahepatic bile duct or redistributed into the extracellular compartment by sinusoidal backflux.
  • the non-uptake fraction of tracer directly wash out the liver through the hepatic veins to be next redistributed.
  • the residual function R(t) can be decomposed into two residue functions such as:
  • R(t) R e (t) + R p (t)
  • R e (t) represents the gadoxetate fraction still present into the extracellular compartment
  • R p (t) represents the gadoxetate fraction still present into the hepatocyte compartment.
  • k 32 denotes the portion transferred from the hepatocyte compartment to the intrahepatic bile ducts
  • a retrospective respiratory motion correction algorithm was developed. First, for all frames of each 2D +t stack, a profile of intensity values along 10 lines integrated in the cranio-caudal direction and encompassing the hepatic dome was recorded. Next, to localize the lung-liver interface frame-by-frame, the maximum of the derivative was computed profile-by-profile. By comparison with a reference (chosen as the first frame), the offset, thus the rigid motion in the cranio-caudal direction over the time was quantified. Finally, the inverse motion was applied frame-by-frame by using a circular permutation.
  • the input functions were normalized by one minus hematocrit. A hematocrit of 45% was assumed in this study. To be able to include the delays ( ⁇ ⁇ and ⁇ ⁇ ) in the model (Eq.23), the input functions were converted into a continuous temporal form instead of a discrete form by interpolation using spline curves.
  • a dedicated data fitting algorithm was developed to avoid multiple local minima problem.
  • the arterial delay was measured as the temporal difference between the beginning of arterial input and tissue response increases.
  • the portal delay was fixed to zero.
  • C(t) F[C A (t - ⁇ ⁇ ) HPI + C P (t - Tp)(l - HPI)] ® ( ⁇ e ⁇ + ⁇ 21 p (l - e ⁇ i *)
  • Fitting procedure was performed with a constrained non-linear least square method using a multi-start trust-region reflective algorithm. For each optimization step, the fitting procedure was run with a grid of stochastic initial conditions generated within two bounds. Each fit procedure was carried out 50 times, with 50 different initializations.
  • the cause of chronic liver disease was viral hepatitis B and/or C in eight patients, nonalcoholic steatohepatitis in seven, alcoholic hepatitis in three, auto-immune hepatitis, toxic hepatitis and Wilson's disease in one patient each.
  • five patients were scored F0, four F1 , four F2, four F3 and four F4.
  • the table 1 which illustrates the perfusion and hepatic transport function parameters according to fibrosis severity is reproduced below.
  • FIG. 5 to 13 are boxplots of perfusion, and hepatic transport function parameters according to degree of liver fibrosis (minimal fibrosis, F0-F1 ), (intermediate fibrosis F2-F3) and cirrhosis (F4). Boxplots show the increase of arterial fraction in patients with intermediate fibrosis and cirrhosis, and the increase of mean transit time and extracellular volume in cirrhosis. Hepatocyte uptake fraction, sinusoidal backflux and biliary efflux decrease according to fibrosis severity. Cellular volume is reduced in cirrhosis. Lines within boxes represent median; lower and upper limits of boxes represent 25 th and 75 th percentiles and whiskers represent 10 th and 90 th percentiles.
  • the arterial fraction (69.4 ⁇ 24.6 mL.min “1 .100 g "1 versus 79.4 ⁇ 33.7 mL.min “1 .100 g “1 versus 59.5 ⁇ 18.8 mL.min “1 .100 g “1 ), the arterial fraction (given by the hepatic perfusion index) increased according to fibrosis severity (26.0 ⁇ 15.6%, 39.0 ⁇ 1 1 .4% and 63.8 ⁇ 43.6% in minimal fibrosis, intermediate fibrosis and cirrhosis respectively; p ⁇ 0.05 versus minimal and intermediate fibrosis).
  • the extracellular mean transit time was similar between the fibrosis groups (15.0 ⁇ 5.0 s and 14.1 ⁇ 3.9 s in minimal and intermediate fibrosis respectively) and increased in cirrhosis (30.1 ⁇ 12.5 s, p ⁇ 0.05 versus minimal and intermediate fibrosis).
  • the extracellular volume was similar between the fibrosis groups (18.1 ⁇ 4.6% and 19.3 ⁇ 6.8% for minimal and intermediate fibrosis respectively) and increased in cirrhosis (27.9 ⁇ 6.0 %, p ⁇ 0.01 and p ⁇ 0.05 versus minimal and intermediate fibrosis).
  • Figures 13 to 16 illustrate parametric maps of biliary efflux after pixel-by-pixel computation according to the fibrosis severity.
  • Figure 13 illustrates a map for a F0 patient, k 3 being equal to 3.53 mL.min “1 .100g “1 ;
  • figure 14 illustrates a map for a F1 patient, k 3 being equal to 3.67 mL.min “1 .100g “1 ;
  • figure 15 illustrates a map for a F2 patient, k 3 being equal to 2.23 mL.min "1 .100g “1 and
  • figure 16 illustrates a map for a F3 patient, k 3 being equal to 1 .64 mL.min "1 .1 OOg "1
  • the biliary efflux decreased according to fibrosis severity (3.2 ⁇ 1 .0 mL.min “1 .100 g “1 , 1 .5 ⁇ 0.5 mL.min “1 .100 g “1 and 0.31 ⁇ 0.57 mL.min “1 .100 g “1 in minimal fibrosis, intermediate fibr osis and cirrhosis respectively, p ⁇ 0.01 between each group),.
  • the sinusoidal backflux decreased according to fibrosis severity (0.035 ⁇ 0.012 mL.min “1 .100 g “1 , 0.013 ⁇ 0.5 mL.min “1 .100 g “1 and 0.0012 ⁇ 0.002 mL.min “1 .100 g “1 in minimal fibrosis, intermediate fibrosis and cirrhosis respectively, p ⁇ 0.01 between each group).
  • the hepatic perfusion index, the hepatocyte uptake fraction, the sinusoidal backflux, and the biliary efflux hepatobiliary enhancement were selected for ROC analysis. This selection is notably illustrated by table 2 and figure 17.
  • the table 2 illustrates the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, predictive positive value (PPV), negative predictive value (NPV) and accuracy (ACC) of hepatic perfusion index (HPI), hepatocyte uptake fraction, sinusoidal backflux and biliary efflux for diagnosing the significant fibrosis (F ⁇ 2).
  • Figure 17 corresponds to ROC curves illustrating the performances of the hepatic perfusion index, the sinusoidal backflux, the hepatocyte uptake fraction and the biliary efflux as perfusion and liver function parameter to assess the significant fibrosis.
  • Function parameters gave better performances than the hepatic perfusion index.
  • Biliary efflux gave the best AUROC (0.95 vs. 0.88, 0.81 and 0.75 for the sinusoidal backflux, the hepatocyte uptake fraction and the hepatic perfusion index).
  • hepatic function parameters were more relevant than micro perfusion parameters to diagnose significant fibrosis.
  • the biliary efflux was the most pertinent. This underlines the importance of complete hepatic liver function quantification.
  • etiologies of disease were heterogeneous and this variability may explain the lower performance of the hepatic perfusion index to diagnose significant fibrosis in comparison with function parameters.
  • fibrosis localization varies according to the etiology (perisinusoidal in NASH and centrolobular in viral hepatitis) and thus differently affects liver perfusion as reflected by the large standard deviation for perfusion parameters in our groups.
  • the presented method takes into account both arterial and portal inputs. Indeed, the obtained results shows that perfusion contribution from portal or arterial input over the total perfusion is drastically modified according to the disease.
  • Another advantage of the method is the absence of breath-holding requirement during dynamic acquisition since ghosting artifact was importantly reduced by the use of a key-hole acquisition with stochastic trajectories for k-space filling and misregistration between 2D+t frames were compensated by the retrospective respiratory motion correction including in our post-processing pipeline.
  • a more conventional automatic registration algorithm we develop and include a dedicated algorithm in the reconstruction pipeline.
  • the rationale behind this choice was that the functions of similarity used by automatic registration algorithms are sensitive to pixel intensity variation according to the time. Therefore, dynamic contrast enhancement confounds pixel intensity variations linked to the motion and induces substantial registration errors, particularly during the perfusion phase where signal intensity variations over the time are the most important.
  • Amer-based semi-automatic methods could be an alternative to iconic, nevertheless, their use are limited by the prohibitive number of dynamics. Nevertheless, this method did not accounting for the non-rigid component of the motion and can be only used for coronal plane acquisitions.
  • Hepatic transport function parameters may be useful to assess liver fibrosis in patients with chronic liver diseases.

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Abstract

L'invention concerne les maladies hépatiques. Les maladies hépatiques englobent notamment la maladie hépatique chronique et le cancer du foie (un cancer primitif du foie ou une métastase). Il est donc nécessaire de pouvoir extraire des biomarqueurs pour des sujets souffrant de cette maladie. Ainsi, les inventeurs ont travaillé sur un procédé de post-traitement d'images d'une zone d'intérêt en vue d'obtenir au moins un paramètre de perfusion et au moins un paramètre de transport. Un tel procédé permet d'obtenir un procédé pouvant être mis en œuvre sur ordinateur et fournit d'une manière plus aisée et plus précise un accès à des paramètres pertinents pour les maladies hépatiques. Ce procédé peut être appliqué pour prédire qu'un sujet présente un risque de souffrir d'une telle maladie, diagnostiquer une maladie, identifier une thérapie ou un biomarqueur et filtrer des composés utiles en tant que médicament.
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